Transcription from loci encoding no known functional elements is widespread in the human genome, and in many model systems. A key challenge in genome biology is to determine which such dark matter transcripts are functionally relevant. This search for functional RNAs is motivated in part by the potential of RNAs as targets for treatment of human disease, and as therapeutic agents themselves. The investigator proposes to develop methods to infer function of un-annotated transcripts on a high-throughput scale, using yeast as a model. Previously, the investigator pioneered the genetic analysis of mRNA expression differences between genetically diverse individuals. On the basis of this demonstrated expertise with experimental genomics, software development, and molecular genetics, the investigator now proposes to develop a related strategy for un-annotated, putative noncoding RNAs in yeast. The principal goal is to harness the co-regulation of known genes and un-annotated transcripts to infer function of the latter. The project will map DNA differences between yeast strains that cause variation in levels of RNAs-both annotated and un-annotated. Software for genetic mapping will identify polymorphisms in master regulators, each of which affects the expression of multiple downstream targets in trans. In such a regulon, functional genomic analysis will find common pathway membership among known genes, leading to the inference that un- annotated transcripts also function in the same pathway. Mapping software will also identify polymorphisms in cis-regulatory elements, each of which affects levels of a transcript encoded nearby; this will allow the discovery of promoters and other cis-acting regulatory regions for novel RNAs. Molecular methods will provide experimental confirmation of the predicted function and regulation of individual RNAs. Discoveries of these RNAs, and the software tools that enable them, will serve as a springboard for future work in metazoans.